System to deliver adaptive epidural and/or subdural electrical spinal cord stimulation to facilitate and restore locomotion after a neuromotor impairment

ABSTRACT

The present disclosure provides a closed-loop system for real-time control of epidural and/or subdural electrical stimulation comprising electrodes for applying to a subject neuromodulation with adjustable stimulation parameters, the electrodes being operatively connected with a real-time monitoring component comprising sensors continuously acquiring feedback signals from said subject, said signals being neural signals and/or signals providing features of motion of said subject, said system being operatively connected with a signal processing device receiving said feedback signals and operating real-time automatic control algorithms, said signal processing device being operatively connected with the electrodes said and providing the electrodes with new stimulation parameters, with minimum delay. The system of the disclosure improves consistency of walking in a subject with a neuromotor impairment. A Real Time Automatic Control Algorithm is used, comprising a feedforward component employing a single input-single output model (SISO), a multiple input-single output (MISO) model, or a multiple input-multiple output (MIMO) model.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 15/033,063, entitled “SYSTEM TO DELIVER ADAPTIVEEPIDURAL AND/OR SUBDURAL ELECTRICAL SPINAL CORD STIMULATION TOFACILITATE AND RESTORE LOCOMOTION AFTER A NEUROMOTOR IMPAIRMENT,” filedon Apr. 28, 2016. U.S. patent application Ser. No. 15/033,063 is a U.S.National Phase of International Patent Application Serial No.PCT/EP2014/073183, entitled “SYSTEM TO DELIVER ADAPTIVE EPIDURAL AND/ORSUBDURAL ELECTRICAL SPINAL CORD STIMULATION TO FACILITATE AND RESTORELOCOMOTION AFTER A NEUROMOTOR IMPAIRMENT,” filed on Oct. 29, 2014.International Patent Application Serial No. PCT/EP2014/073183 claimspriority to European Patent Application No. 13191003.6, entitled “SYSTEMTO DELIVER ADAPTIVE ELECTRICAL SPINAL CORD STIMULATION TO FACILITATE ANDRESTORE LOCOMOTION AFTER A NEUROMOTOR IMPAIRMENT,” filed on Oct. 31,2013. The entire contents of each of the above-cited applications arehereby incorporated by reference in their entirety for all purposes.

FIELD OF THE INVENTION

The present disclosure refers to the field of neuroprosthetics, inparticular to devices and systems for facilitating and restoringlocomotion in subjects after neurological disorders, more in particularafter spinal cord injury (SCI), Parkinson's disease, multiple sclerosis,and stroke.

BACKGROUND OF INVENTION

Epidural electrical spinal cord stimulation (EES) at the lumbosacralsegments has been shown to be a very promising intervention capable offacilitating locomotion in rats, cats, and humans with SCI (Ichiyama, R.M., Gerasimenko, Y. P., Zhong, H., Roy, R. R. & Edgerton, V. R. Hindlimbstepping movements in complete spinal rats induced by epidural spinalcord stimulation. Neuroscience letters 383, 339-344,doi:10.1016/j.neulet.2005.04.049 (2005); Minassian, K. et al. Humanlumbar cord circuitries can be activated by extrinsic tonic input togenerate locomotor-like activity. Human movement science 26, 275-295,doi:10.1016/j.humov.2007.01.005 (2007); Harkema, S. et al. Effect ofepidural stimulation of the lumbosacral spinal cord on voluntarymovement, standing, and assisted stepping after motor completeparaplegia: a case study. The Lancet 377, 1938-1947 (2011); Gerasimenko,Y. P. et al. Epidural spinal cord stimulation plus quipazineadministration enable stepping in complete spinal adult rats. JNeurophysiol 98, 2525-2536, doi:10.1152/jn.00836.2007 (2007)).

When combined with pharmacological interventions and locomotor training,EES was demonstrated to affect functional recovery, i.e., spinal ratswere able to recover full weight-bearing stepping capacities on atreadmill (Edgerton, V. R. et al. Training locomotor networks. Brainresearch reviews 57, 241-254, doi:10.1016/j.brainresrev.2007.09.002(2008); Ichiyama, R. M. et al. Step training reinforces specific spinallocomotor circuitry in adult spinal rats. The Journal of neuroscience:the official journal of the Society for Neuroscience 28, 7370-7375,doi:10.1523/JNEUROSCI.1881-08.2008 (2008); Courtine, G. et al.Transformation of nonfunctional spinal circuits into functional statesafter the loss of brain input. Nature neuroscience 12, 1333-1342,doi:10.1038/nn.2401 (2009); Musienko, P., Heutschi, J., Friedli, L., denBrand, R. V. & Courtine, G. Multi-system neurorehabilitative strategiesto restore motor functions following severe spinal cord injury.Experimental neurology, doi:10.1016/j.expneurol.2011.08.025 (2011)).

In the prior art several patents regarding neuroprosthetic apparatus orsystems can be found.

US2005/090756 discloses a neural spike detection system forneuroprosthetic control, wherein neural signals are received and aninformation signal is transmitted when a neural spike is detected.

US2004/0267320 discloses algorithm for programming a device according tothe firing rate of motor neurons. In particular, electrical impulses aredetected and movements are calculated from said impulses. Said impulsesmay be detected in a subject cerebral cortex and brain-to-arm controlmay be provided.

US2003/114894 discloses a surface neuroprosthetic that enables facileadjustment and fine-tuning of the local current density over the surfaceof a transcutaneous scanning electrode, so as to achieve optimal muscleresponse. In particular, a scanning electrode for neuroprosthesisapplied on muscle of a limb is disclosed.

With regard to a brain spinal interface, US2011/0208265, for example,discloses a multi-programmable trial stimulator for spinal cord, amongothers. The stimulator can provide a wide range of frequencies, however,a specific selection of frequencies for achieving control of locomotionfunctions is not disclosed in said document.

US2012/0330391 discloses a method for using spinal cord stimulation totreat symptoms of motor disorders including implanting a stimulationlead within a ventral portion of the epidural space. Frequencies higherthan 100 Hz with a pulse width of less than 20 μs are disclosed.

WO2012/094346 discloses a method wherein electrical stimulation isapplied to a portion of a spinal cord of a patient with a neurologicallyderived paralysis. Optionally, the disclosed method can be repeatedusing electrical stimulation having different sets of parameter valuesto obtain quantifiable results generated by each repetition of themethod. Then, a machine learning method may be executed by at least onecomputing device. The machine learning method builds a model of arelationship between the electrical stimulation applied to the spinalcord and the quantifiable results generated by activation of the atleast one spinal circuit. A new set of parameters may be selected basedon the model.

In US2002/0115945 a method for restoring gait in individuals with SCI isdisclosed, wherein epidural spinal cord stimulation is combined withpartial weight bearing therapy.

In EP2486897, a closed loop brain-machine interface is disclosed,wherein neural signals are acquired and translated into movementsperformed by a machine. Sensory feedback is also provided. Saidinterface can be used for restoring voluntary control of locomotion. Inthe disclosed interface, however, signals are acquired directly from thebrain of the subject, motor commands are extracted and movements areeffected by an actuator.

In WO2013/071309, transcutaneous electrical spinal cord stimulation(tESCS) is used as a noninvasive method in rehabilitation of spinalpathology. The electrical stimulation may be delivered at 5-40 Hz at20-100 mA. As in WO2012/094346, the possibility of a method and a modelof relationship between electrical stimulation and results is disclosed.

WO2007/047852 discloses a method of treating a patient by providing anelectromagnetic signal. Closed-loop neuroprosthetic devices are known inthe prior art for use, for example, for predicting and preventingepileptic seizures (see for example U.S. Pat. No. 8,374,696 andUS2007/0067003).

There is still the need of a method for improving and restoringlocomotor functions in subjects with neuromotor impairments, inparticular after spinal cord injury.

It is known that EES can be useful for facilitating locomotion insubjects with spinal cord injury and Parkinson's disease.

It is also known that each EES pulse generates a reflex response in themuscle. During stepping, EMG bursts are built from a succession of thesereflex responses, which are modulated naturally during the differentphases of the gait-cycle, but which may also be directly affected by theparameters of stimulation (namely frequency, amplitude and pulse-width).

There is the need of a real-time control system wherein EES can bemodulated, and thus optimized, during the gait-cycle during locomotion,so that a precise control of gait patterns, muscle activity, and foottrajectory can be achieved, and also for accurate upper-limb control(for precise reaching and grasping).

The control and modulation of the electrical stimulation is particularlyadvantageous for facilitating and improving locomotion functions.

For example, a controlled electrical stimulation helps compensating forthe fatigue deriving from an external source of muscle activity. Whennon-controlled EES-induced locomotion is performed, fatigue yields adecreased flexion and extension pattern during stepping over time thusinducing lower stepping and eventually collapse.

It has now been found that there is a linear relationship between thefrequency of electrical stimulation applied in the epidural and subduralspace and relevant parameters of gait.

In particular, it has now been found that there is a linear relationshipbetween the frequency of EES and relevant parameters of gait, inparticular step height. This relationship has been used for thedevelopment of models and control algorithms which allow for acontrolled modulation of locomotor patterns through the adaptation ofEES frequency, thus achieving real-time control of locomotion.

It has been found that EES frequency clearly and consistently modulateslocomotor patterns in subjects with SCI or with lesions of the upperlimbs or head in unique and predictive ways.

Gait features that were most correlated with changes in EES frequencycan be grouped into functional clusters of flexion, extension, speed andvariability along Principal Component Analysis (PC).

In particular, it has been found that the relationship between EESfrequency and step height (i.e., the maximum height reached by the footduring each gait cycle) is close to linear and step height is modulatedwith the frequency, which allowed us to build a linear input-outputmodel, particularly useful for EES control.

It has also been found by the inventors of the present disclosure thatEES applied at lumbar and sacral locations promotes whole-limb flexionand extension, respectively. Also, when EES is applied on the lateralside of the spinal cord the effects of the stimulation are restricted tothe limbs on the stimulated side. Real-time algorithms have thus beendeveloped to apply EES to different sites of stimulation, in someexamples to 4 or more sites based on the actual phase of the gait cycle.

It has further been found that the timing at which stimulation at eachsite is turned on and off is critical. Each site of EES stimulationmodulates a specific functional effect, including facilitation ofextension and flexion of the left versus right limbs, based on theactual phase of the locomotor movement.

This rolling burst EES pattern markedly increases the EMG activity oflimb muscles, and promotes locomotion with improved interlimb andintralimb coordination, and superior weight-bearing levels compared tocontinuous EES.

In particular, it has also been found that subdural stimulation promotescoordinated, weight bearing stepping of a paralyzed limb with improvedgait characteristics. More in particular, subdural stimulation requiresreduced electrical current threshold to be effective and achieves morespecific unilateral recruitment of motor neurons.

SUMMARY OF THE INVENTION

An object of the present disclosure is a closed-loop system forreal-time control of epidural and/or subdural electrical spinal cordstimulation characterized in that it comprises

-   a. means for applying to a subject a neuromodulation with adjustable    stimulation parameters (or values), said means a) being operatively    connected with-   b. a real-time monitoring component comprising sensors continuously    acquiring feedback signals from said subject, said signals being    neural signals and/or signals providing features of motion of said    subject, said system b) being operatively connected with-   c. a signal processing device receiving said feedback signals and    operating real-time automatic control algorithms, said signal    processing device being operatively connected with said means a) and    providing said means a) with new stimulation parameters (values),    with minimum delay.

In an embodiment of the disclosure said means a) for neuromodulationcomprise an epidural and/or subdural electrical stimulation device.

Indeed, electrical stimulation can be applied in the epidural and/or inthe subdural space.

In an embodiment of the disclosure, said stimulation parameters arewaveform, amplitude, pulse width and frequency. Each parameter can beindependently adjusted at each cycle.

In one embodiment, said stimulation parameter is frequency.

In one embodiment of the present disclosure, said means a) can provide astimulation frequency comprised between 5 and 120 Hz, more specificallybetween 25 and 95 Hz, wherein the resolution is, for example, of 1 Hz.

In one embodiment of the present disclosure, said means a) comprises oneor more electrodes, for example an electrode array. Said means a) canalso comprise an implantable pulse generator.

Said electrodes can apply the epidural and/or subdural ES (electricalstimulation) to any stimulation site along the spinal cord of thesubject. Example stimulation sites are lumbar and sacral sites for lowerlimb stimulation and cervical sites for upper-limb stimulation. Lowerlimb stimulation is applied, for example, for facilitating standing andwalking in a subject; upper-limb stimulation is applied, for example,for facilitating reaching and grasping.

In one embodiment of the disclosure, said stimulation sites are at leasttwo and each stimulation site can be independently turned on or off.

In an embodiment for facilitating locomotion, the stimulation applied bymeans a) is phase dependent. This means that specific electrodes areactivated during specific sub-phases of the gait cycle. In an exemplaryembodiment, the lateral extensor-related (sacral) electrodes areactivated during stance, and lateral flexor-related (upper lumbar)electrodes are activated during swing. When is inactive, the amplitudeof the corresponding electrodes is zero. Thus in this embodiment,electrodes applied on sacral and lumbar sites are alternativelyactivated to promote, respectively, whole-limb extension or flexion.

In an alternative embodiment, the stimulation applied by means a) is aburst stimulation.

For burst stimulation it is intended that each electrode is activatedfor a certain time (“burst”), wherein the activation times of eachelectrode and the duration of each activation is pre-defined by a user,said user being a clinician or a physiotherapist, for example.

In one embodiment of the present disclosure stimulation islocation-specific, wherein the stimulation parameters of each individualelectrode (waveform, amplitude, pulse width, frequency) can beindependently modified in real time.

In another embodiment of the present disclosure stimulation istime-specific (burst stimulation), wherein each single electrode can beindividually turned ON and OFF in real time based on external triggersignals.

In an additional embodiment of the present disclosure stimulation isfrequency-dependent.

In an embodiment of the disclosure, the real-time monitoring componentb) is a motion capture system, an accelerometer or a gyroscope.

In another embodiment, said sensors of b) can be selected from the groupconsisting of: force sensors, electromyographic sensors, joint anglesensors, flow sensors and pressure sensors.

In another embodiment of the present disclosure, said monitoringcomponent b) is a motion capture system comprising three or more camerasand position markers placed on the subject, for example on the hindlimb,more specifically on any of one or more crest, hip, knee, ankle and footand/or on the forelimb, in particular on any of one or more shoulder,elbow, wrist, hand and digits. Said position markers may provide signalsincluding features of motion from the subject, which may be interpretedvia the motion capture system, for example. Said signals providingfeatures of motion of the subject may include coordinates, kineticinformation, acceleration, speed, angle, angular speed, and/or angularacceleration of one or more of hip, knee, ankle, foot, shoulder, elbow,hand, wrist and/or digits of the subject.

In a further embodiment, said feedback signals acquired by the sensorsof b) are neural signals.

Neural signals provide information about the subject locomotor state andits motor intention. As an example, said neural signals are corticalsignals. Cortical signals can be recorded, for example, from sensory,motor, sensorimotor or pre-motor cortex. Said signals can be recordedintra-cortically or using Electroencephalography (EEG) systems.Exemplary neural signals which may be recorded are Single-Unit activity,Multi-Unit activity or Local Field Potentials.

Neural signals can be detected by neural probes situated in the cerebralarea of interest. Neural probes may be electrode arrays implanted in thearea of interest. For example, electrodes may be implanted in the limbarea of the sensorimotor cortex.

The local field potential (LFP) and multiunit activity (MUA) areextracellularly recorded signals from a local network of neurons.

Therefore, according to the teaching of the present disclosure saidneural signals can provide indirect features of motion of said subjectwhich can be used alone or in combination with signals providing directfeatures of motion of the subject, as explained in the foregoingdescription, together composing the feedback signals.

In one embodiment of the present disclosure, said signal processingdevice c) operates a program comprising an automatic control algorithmthat interfaces simultaneously with the data flow from said real-timemonitoring component b) and the means for epidural electricalstimulation a) in real time.

In one embodiment of the present disclosure for facilitating locomotion,the signal processing device c) acquires feedback signals from saidmonitoring component b), detects in real-time key events of gait usingfeature detection algorithms, and automatically adapt stimulationparameters online, thus providing said means a) with new stimulationparameters values.

In another embodiment of the present disclosure, the signal processingdevice c) acquires feedback signals from b) providing information onfoot kinematics features of the subject and from said signals it detectsgait events based on the elevation of the foot in order to detectfoot-strike events and toe-off events, for example from both hindlimbs,and thereby defining specific sub-phases of gait. In some examples, saidsub-phases of gait are the stance-phase and the swing-phase.

In such an embodiment of the disclosure, said device c) identifies thestance-phase and the swing-phase within each gait-cycle of locomotion,and provides means a) with new stimulation parameters values. Forexample, means a) may comprise several electrodes applied on differentstimulation sites, which are turned on or off according to theinformation provided by device c) so that whole-limb extension isprovided during the stance phase and whole-limb flexion is providedduring the swing phase.

Another object of the present disclosure is the above system for use forfacilitating locomotor functions in a subject suffering from aneuromotor impairment.

In an embodiment of the present disclosure, said neuromotor impairmentis selected from the group consisting of partial or total paralysis oflimbs. Said limb paralysis can be unilateral or bilateral. Inparticular, said neuromotor impairment is consequent to a spinal cordinjury, an ischemic injury resulting from a stroke, a neurodegenerativedisease, for example Parkinson disease.

A further object of the present disclosure is a system as above definedfor restoring voluntary control of locomotion in a subject sufferingfrom a neuromotor impairment further comprising an apparatus selectedfrom the group consisting of at least one of a treadmill or arobot-assisted body-weight support or a multidirectional trunk supportsystem.

It is also an object of the present disclosure, a method for determiningoptimal stimulation parameters for a subject suffering from a neuromotorimpairment and undergoing a process for facilitating locomotor functionscharacterized in that it comprises the following steps:

determining a first electrical stimulation which has been applied tosaid subject bearing means for applying an epidural and/or subduralelectrical stimulation (or means for neuromodulation) with adjustablestimulation parameters;

acquiring feedback signals from said subject, said signals being neuralsignals and/or signals providing features of motion of said subject,through a real-time monitoring system, while this first stimulationoccurs;

transmitting said feedback signals to a signal processing device;

calculating by means of said signal processing device operating a RealTime Automatic Control Algorithm new stimulation parameters;

providing instructions to said means of step a) for applying a secondepidural and/or subdural electrical stimulation so that said means arecapable to administer a second electrical stimulation with said newstimulation parameters calculated in step d) to said subject.

In some examples, such a method may further include indicating ordetermining that the second electrical stimulation comprising said newstimulation parameters are sufficiently optimized such that said secondelectrical stimulation may be repeatedly applied to the subject, forexample, for a duration of a physical exercise. In some examples, therepeatedly applied stimulation may be delivered at a determined pacecorresponding to a desired period of the facilitated locomotor function.In other words, responsive to said new stimulation parameters beingsufficiently optimized, the second electrical stimulation may be appliedin an open-loop fashion, where feedback signals from the subject areeither not acquired, or if acquired, do not impact the second electricalstimulation. For example, consider an example where the physicalexercise includes standing in place. By using the method for determiningoptimal stimulation parameters for the subject described above, thesecond electrical stimulation, which may comprise optimal stimulationparameters, may be applied in an open-loop fashion for a particularduration (e.g. desired duration) of the physical exercise, which in thisexample includes standing in place. The second electrical stimulationmay be provided for the desired duration, and may be delivered at adetermined pace. In some examples, the determined pace may comprise apace that is a function of avoiding fatigue that may otherwise result ifthe pace of delivery were quicker (e.g. delivered more frequently), ordelivered continuously. While the above-described example physicalexercise includes standing, it may be understood that the physicalexercise may include walking for a determined duration, in otherexamples. The physical exercise may not be limited to standing andwalking. For example, the physical exercise may comprise a situationwhere the subject is laying down, and attempts to lift the subject's legoff the ground. Such examples are illustrative in nature, and othertypes of physical exercise have been contemplated.

In the above-described method, it is disclosed that the subject bearsthe means for applying an epidural and/or subdural electricalstimulation (or means for neuromodulation). Thus, it may be understoodthat such means may include a fully implantable neuromodulator,partially implantable neuromodulator, or external neuromodulator, asdiscussed in further detail below with regard to FIG. 18.

The above method can be implemented in a system for real-time control ofepidural and/or subdural electrical stimulation, or neuromodulationwhich may include externally applied stimulation (e.g. external to thesubject). In some examples, neuromodulation may comprise stimulation ofthe spinal cord.

In one embodiment, in step d) said Real Time Automatic Control Algorithmcomprises a feedforward component employing an input-output model whichis a single input-single output model (SISO), wherein one stimulationparameter is changed to control one gait feature, or, alternatively amultiple input-single output (MISO) model, wherein multiple stimulationparameters are adjusted to obtain a single desired gait feature(output), or a multiple input-multiple output (MIMO) model, whereinmultiple stimulation parameters are adjusted to obtain multiple desiredgait features (output).

Another object of the present disclosure is a method for facilitatingstanding and walking functions in a subject suffering from neuromotorimpairment comprising the following steps:

using a system for restoring voluntary control of locomotion comprisingthe closed-loop system above described;

providing to said subject a first epidural and/or subdural electricalstimulation with adjustable stimulation parameters;

acquiring feedback signals from said subject, said signals being neuralsignals and/or signals providing features of motion of said subject;

transmitting said feedback signals to a signal processing device;

calculating by means of said signal processing device operating a RealTime Automatic Control Algorithm new electrical stimulation parameters;

providing to said subject a second epidural and/or subdural electricalstimulation with said new electrical stimulation parameters calculatedin step e), and optionally

administering to said subject before and/or during administration ofsaid first and/or said second electrical stimulations a pharmaceuticalcomposition comprising at least one agonist to monoaminergic receptors.

Similar to that discussed above, such a method may further includeindicating or determining that the second electrical stimulationcomprising said new stimulation parameters are sufficiently optimizedsuch that said second electrical stimulation may be repeatedly appliedto the subject, for example, for a duration of a physical exercise. Insome examples, the repeatedly applied stimulation may be delivered at adetermined pace corresponding to a desired period of the facilitatedlocomotor function.

In one embodiment, in step e) said Real Time Automatic Control Algorithmcomprises a feedforward component employing an input-output model whichis a single input-single output model (SISO), wherein one stimulationparameter is changed to control one gait feature, or, alternatively amultiple input-single output (MISO) model, wherein multiple stimulationparameters are adjusted to obtain a single desired gait feature(output), or, alternatively a multiple input-multiple output (MIMO)model, wherein multiple stimulation parameters are adjusted to obtainmultiple desired gait features (output).

Another object of the present disclosure is the system disclosed abovefor facilitating and/or restoring voluntary control of locomotion in asubject suffering from a neuromotor impairment.

The present disclosure will be disclosed in detail also by means ofFigures and Examples.

FIGURES

FIG. 1 shows tasks performed in the presence of EES and pharmacologicagents: Treadmill, Overground (Robot-assisted body-weight support), orfunctional applications on Robot+Staircase.

FIG. 2 shows an example of locomotor patterns recorded on treadmill asthe frequency of stimulation is modulated from a functional lower limit(dragging occurs and little ground reaction forces) to a high valueswhere walking becomes little consistent and jumpy.

FIG. 3 shows a Statistical Representation of Modulatory Capacities ofEES Frequencies in PC space. PC analysis was applied on all gait cyclesrecorded from all rats (n=5) under different EES frequencies (hereranges from 40 to 70 Hz as shown). Each point in 3D space represents agait cycle under a given condition once projected in the 3-dimensionalPC space. Data points clustered in distinct spatial locations, revealingthat different stimulation frequencies modulated locomotor patterns inthe same direction in all the rats.

FIG. 4 shows modulation of step-height with frequency of stimulation,and linear regression that may be used as linear input-outputrelationship.

FIG. 5 shows a closed-loop monitoring and control setup. The controlleremploys the linear model between frequency and step-height, and employsit in conjunction with an error-corrector (PI controller) to adapt thestimulation at each gait cycle. Bottom graph shows the desired referencetrajectory and the modulation over time.

FIG. 6 shows a “Scissor task”. The desired ref (shaded area) isconstantly changed at every gait cycle, compelling the controller tocontinuously adapt and use its feedforward component (linear model).Different changing rates were applied, ranging from 11 mm/step to 35mm/step (upper limit). Results are consistent for n=4 animals. For allcases, the step-height was accurately positioned.

FIG. 7 shows step-heights as the beam (within which errors are notcorrected) is reduced from +/−5 mm (left) to +/−1 mm (right). Eventhough the control increasingly needs to act to compensate for steps outof the beam, the actual variability remains in similar values.

FIG. 8 shows inherent variability in stepping under differentconditions: Healthy (black), non-controlled 40 Hz stimulation (white)and controlled (grey) with different beams (+/1 mm for C1, etc).

FIG. 9 shows a fatigue experiment & rehab application (num animals=3).The statistics point out that the controlled output maintains correctstepping (in the desired band) 5 to 6 times longer than in the noncontrolled case. The full length of the trials are themselves twice aslong in the controlled case than in the non-controlled one.

FIG. 10 shows robot trials and stair-climbing application−num animals=4.The statistics of the kinematic trajectories (bottom right−mean+/−sem)clearly show that controlled outputs followed the desired height abovethe staircase. This also implied an adaptive modulation in force (bottomleft) for each condition.

FIG. 11 Shows a Left (a): Polynomial parameterization of output footelevation of the whole gait cycle. This enables to quantify time in afew parameters that expand our output description. Right (b): Bodyposition to account for biomechanics in the input description.

FIG. 12 shows a superposition of model output for each gait-cycle andactual data recorded, as the two electrodes S1 and L2 changeindependently (black straight and dotted lines).

FIG. 13 shows a location-specific stimulation with a multi-electrodearray. Stimulation is triggered for each electrode based on thesub-phase of the gait cycle (lateral sacral electrode during stance,lateral lumbar electrodes during swing). This time- andlocation-dependent stimulation results in enhanced whole-limb extensionand increased whole-limb flexion, as observed through kinematicend-point trajectories. The activity of the key muscles responsible forthese movements is increased manifold.

FIG. 14 shows a scheme of an embodiment of the disclosure, illustratedin the case of rehabilitation of rodents on a treadmill. The subject iscontinuously monitored in real time (in this case, kinematics recordedusing reflective markers attached to landmark joints, tracked usingmotion capture cameras operating at 200 Hz—Electromyographic and GroundReaction Forces recorded at 2 kHz). All the information is synchronized,all signals are filtered in real time using adaptive filters, andkinematics interpolated to deal with missing markers (e.g., due toocclusions). Once the biomechanic state of the system is complete, theclosed loop system proceeds to (i) automatically detect key gait eventsand (ii) extract meaningful features for control. Two types of gaitevents are detected using complementary online algorithms. These includeevents that have a clear kinematic signature (e.g., foot strike and toeoff, for which simple kinematic thresholding is accurate enough) butalso user-defined moments within the gait cycle (e.g., middle of swing).Custom made algorithms monitor the rotation of the foot around a virtualcentre and detected events through threshold-crossing in angle space.These events trigger the control calculations, which can either turn ONor OFF individual electrodes (case of event 1, which triggers OFFelectrode 2) for phasic stimulation, or start feedback-feedforwardcontrol to achieve a desired behavior (case of event 2, which calculatesthe appropriate frequency correction applied to electrode 1).

FIG. 15 shows a scheme of an embodiment of the disclosure wherein thevoluntary walking intention of the subject is detected from corticalrecordings and the electrical stimulation is provided below the injurylevel. A) A severe contusion interrupts most of the fibers connectingthe motor cortex and the segments of the spinal cord where moto-neuronscontrolling hindlimb locomotion are located. B) a) Micro-wire electrodearrays inserted in the leg area of the rats' motor cortex recordmulti-unit neuronal activity that is decoded in real-time intodiscriminating ‘idle’ or ‘walk’ behavioral states. b) A severe spinalcontusion spares few fibres travelling across the injury (GFAP=glialfibrillary acidic proteins, NISSL=nucleic acid staining), c) thus theneural drive is replaced by pharmacological and electrical interventionat the sublesional spinal level. C) On over-ground tests the decoder isable to capture the cortical multi-unit activity and detect the subjectanimal's intention to walk. It consequently delivers temporizedstimulation through the spinal electrodes, with high precision in thesynchronization to the onset of locomotion.

FIG. 16 shows a comparison between epidural (dark grey) and subdural(light grey) electrical stimulation. (A) Top: Computerized Tomography(CT) Scan of epidural and subdural implants. Bottom: computerizedsimulation show increased selectivity of unilateral voltage fields forsubdural implantation indicated by the more restricted voltage field(red area in diagram). (B) Electrophysiological experiments confirmedthat subdural stimulation required reduced current threshold, andachieved more specific unilateral recruitment of motor neurons comparedto epidural stimulation. Unilateral selectivity was calculated as1—ipsilateral muscle recruitment/contralateral muscle recruitment. Thegraph shows that subdural stimulation achieved an increased amplituderange (delta) for unilateral muscle recruitment.

FIG. 17 shows a closed loop multisite spinal cord stimulation appliedthrough subdural electrodes improves stepping capacities afterunilateral spinal cord injury. (A) Anatomical reconstruction of aunilateral spinal cord injury leading to unilateral hindlimb impairment.(B) Subdural spinal cord stimulation delivered through the lateralelectrodes (electrode promoting limb flexion: Flex, electrode promotinglimb extension: Ext) of a soft subdural implant (40 Hz, 0.2 ms, 20-50μA) promoted coordinated, weight bearing plantar stepping of theparalyzed hindlimb with improved gait characteristics compared tocontinuous stimulation. (C) Quantitative comparison of step height inrats with unilateral spinal cord injury and healthy animals. Closed loopmultisite spinal cord stimulation (Closed loop) improves step heightcompared to continuous open loop stimulation (Continuous).

FIG. 18 shows a schematic diagram of an example system for facilitatingand restoring locomotion in subjects, in accordance with an embodimentof the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

Within the frame of the present disclosure, the following definitionsare provided.

“Device”: means a single device, also intended as “means” or“component”, namely a device which, taken alone performs a definedfunction. Examples of devices are an epidural electrical stimulator(EES), a sensor, a signal processor.

“Apparatus”: means a component comprising one or more devicescooperating to provide a more complex function. Examples of apparatusesare a computer, a monitoring component. An apparatus can also beintegrated in a system.

“System”: means an ensemble of one or more apparatuses and/or devicescooperating to provide an even more complex function. Example of systemis the closed-loop system of the present disclosure.

“Gait cycle”: defines, during locomotion, the motions from initialplacement of the supporting heel of a subject on the ground to when thesame heel contacts the ground for a second time.

“Input-output Feedforward component”: means a component within a controlsystem which is employed to predict the outcome of an input. In thepresent disclosure, it captures the relationship between EES andlocomotor patterns and is employed to predict the outcome of the appliedstimulation.

“Feedback component”: means a component that corrects errors observedbetween the desired output and the obtained output. Within the frame ofthe present disclosure, this component complements the feedforward modelin the control structure. The feedback component corrects for errorsobserved between the desired locomotor pattern and the behaviorobserved.

“Feedback signals”: means signals from the subject which can be signalsproviding direct features of motion, such as for example (i) gaitevents, in particular foot-strike and foot-off events, (ii) specificactivation of muscles or forces, or indirect features of motion such assubject locomotor state and its motor intention; these latter signalsare neural signals originating for example from brain cortex, inparticular sensory, motor or pre-motor cortex.

“Proportional Integral control”: means a type of feedback algorithm thatgenerates a correction that is proportional to the error and to theintegral (cumulated over time) of the error. “Principal Componentanalysis”: means a dimensionality reduction algorithm that helpsanalyzing data in a subspace, where the dimensions that carry most ofthe variance are maintained.

“Operatively connected” means a connection capable of carrying data flowbetween two or more input and/or output data ports. The connection canbe of any suitable type, a wired or a wireless connection.

“Signal processing device”: means any device capable of elaboratinginput signals and produce output signals. Said device can be aprocessor, incorporated in a more complex apparatus or system, such asfor example a computer. According to the present disclosure, said signalprocessing device allows calculating electrical stimulation parameters,stimulation sites, and time of stimulation, to be used with the meansfor applying an epidural electrical stimulation.

A locomotion feature, or gait feature, is a kinematic parametercharacterizing the gait cycle.

For “facilitating standing and walking” is intended an increase of themovements magnitudes of the hind limb joints as well as an improvementof locomotor stability. In particular, the step height and flexor muscleactivity are improved and the limb dragging is reduced. Also, a bettercoordination of extensor and flexor activity (reciprocal) and full bodyweight support is achieved.

The present disclosure will be now disclosed in detail referring to theexemplary embodiment of facilitating and restoring locomotion, withparticular reference to lower limbs, being intended that the teaching ofthe disclosure is applicable to every kind of neuromotor impairments,such as, for example, impairment of upper limbs, head and trunk.

An exemplary representation of the system of the disclosure isrepresented in FIG. 14 and herein explained. The subject 10 to whichepidural and/or subdural electrical stimulation is applied iscontinuously monitored in real time. All the information aresynchronized, all signals are filtered in real time using adaptivefilters, and kinematics interpolated to deal with missing markers (e.g.,due to occlusions). Once the biomechanic state of the system iscomplete, the closed loop system proceeds to (i) automatically detectkey gait events and (ii) extract meaningful features for control. Twotypes of gait events are detected using complementary online algorithms.These include events that have a clear kinematic signature (e.g., footstrike and toe off, for which simple kinematic thresholding is accurateenough) but also user-defined moments within the gait cycle (e.g.,middle of swing). Custom made algorithms monitor the rotation of thefoot around a virtual centre and detected events throughthreshold-crossing in angle space. These events trigger the controlcalculations, which can either turn ON or OFF individual electrodes forphasic stimulation, or start feedback-feedforward control to achieve adesired behavior.

For the purpose of the present disclosure, the means for applying to asubject an epidural and/or subdural electrical stimulation 15 with anadjustable stimulation frequency, are conventional ones. Commerciallyavailable devices for ES and/or EES, as well as custom-designed devicesare suitable for carrying out the present disclosure. In one embodiment,said means for applying an epidural and/or subdural electricalstimulation 15 is a custom-designed device comprising multipleelectrodes, named multi-electrode array (MEA), which is particularlyuseful for site-specific stimulation.

Conveniently, the means for applying an electrical stimulation to asubject can be one or more electrodes, for example an electrode array.Said means can also comprise an implantable pulse generator.

The electrical stimulation is applied to the epidural space and/or tothe subdural space of the vertebral column.

The electrical stimulation can be applied to any portion of the spinalcord of a subject. In an embodiment electrical stimulation may beapplied by an electrode array that is implanted epidurally in the spinalcord of the subject. Such an electrode array may be positioned at leastone of a lumbosacral region, a cervical region, and a thoracic region ofthe spinal cord. The specific site of stimulation can be chosen by theskilled in the art according to the desired effect. For example, in oneembodiment the electrode array is positioned at the lumbosacral regionfor control of locomotion of lower extremities.

The real-time monitoring component 20 b) comprises sensors continuouslyacquiring feedback signals from the subject. The acquired signals areneural signals and/or signals providing features of motion 22 of thesubject as defined above.

In an embodiment, the real-time monitoring component 20 b) detects themovements of the subject 10 after electrical stimulation has beenapplied. It can be a motion capture system or an accelerometer or anyother equivalent means. In one embodiment, the real-time monitoringcomponent is a motion capture system which comprises limb positionmarkers 24. These markers are placed on the subject's limb(s) which is(are) stimulated by means a) and are visible by the real-time monitoringcomponent in a way that it acquires 3D coordinates of the limb movementwhen stimulation by means a) is applied. Typically, markers are ofreflective type, reflective type meaning that they reflect infraredlight emitted by the cameras thus allowing their tracking, but othertypes can be used. Examples of other markers suitable for the presentdisclosure are optical systems, electromagnetic systems, ultrasonicsystems, and combinations of systems suitably integrated by what isknown as the “sensor fusion” method, a triangulation system using radiofrequency antennae and inertial sensor. The marker positions of thesubject are acquired in real-time and associated to specific labels(labels may be for example Crest, Hip, Knee, Ankle, Foot) according touser-defined kinematic model, built on a per-animal basis for highaccuracy tracking. Said model evaluates a set of rules that compares X,Y and Z coordinates of each marker, and derives which set of coordinatescorresponds to which label. Said kinematic model thus matches 3Dpositions of markers with the joint they are attached to. Markerpositions can be, for example, crest, hip, knee, ankle and foot. Asdiscussed above, the marker positions of the subject may providefeatures of motion of the subject, including coordinates, kineticinformation, acceleration, speed, angle, angular speed, and/or angularacceleration of one or more of hip, knee, ankle, foot, shoulder, elbow,hand, wrist, and/or digits of the subject.

Said set of rules operates in three steps: in a first step it evaluatesthe mediolateral coordinates of the markers to distinguish between thoserelated to the right and to the left limb, thus identifying two subsets.In a second step, for each one of these two subsets, top-down rulesdistinguish using the vertical coordinates: crest (highest marker), thecouple hip/knee (lower than Crest) and the couple Ankle/foot (lowest twomarkers). Finally (third step), for each one of these two couples,forward coordinates help distinguish knee from hip (knee is more forwardthan hip) and foot from ankle (foot is more forward than ankle).

For example, a Vicon Kinematic System (Nexus) can be used as a real-timemonitoring component. Other commercially available or custom-builtsystems are suitable for the present disclosure.

The acquired coordinates are then transmitted to an external signalprocessing device 30 (c).

In another embodiment, the real-time monitoring component b) acquiresneural signals from the subject as feedback signals. Said neural signalsprovide information about the locomotor state and the neuronal activityof the subject and transmit them to the processing device c).

Neural signals provide information related to the gait cycle and can beused to control or refine in real time the triggering of electrodes,respectively substituting or co-operating with the kinematic-feedbackalgorithms described above.

In an exemplary embodiment, electrode arrays implanted in the limb areaof the sensorimotor cortex of a subject collect information about thesubject locomotor intention. Using machine-learning approaches thisinformation can be decoded and discriminated into two behavioral states,“rest” or “walk”. The decoding is then transmitted to the processingdevice and switches ON or OFF the feedback-feedforward controller, sothat the desired locomotor pattern is achieved.

With regard to machine-learning approach, reference can be made to thereview “Corticospinal neuroprostheses to restore locomotion after spinalcord injury.” D. Borton, M. Bonizzato, J. Beauparlant, J. Digiovanna, E.M. Moraud, N. Wenger, P. Musienko, I. R. Minev, S. P. Lacour, J. d. R.Millán, S. Micera and G. Courtine published in Neuroscience Research,vol. 78, p. 21-29, 2014.

On the external signal processing device c) a program comprising anautomatic control algorithm interfaces simultaneously with the KinematicData Stream and/or with the Neural Data Stream, i.e. the data flow 32from the real-time monitoring component b), and the means a). Theprogram and the algorithm can be in any programming language able tooperate in real time; for example, it can be in C, C++, C#,Simulink/xPC. It can be compiled according to the general knowledge ofthe skilled in the art using custom-made or commercially availablesoftware, for example TDT. Said program is programmed to detectfoot-strike in real time and to adapt the electrical stimulation at eachgait-cycle thanks to the controller part.

In particular, the Neural Data Stream is the data flow from thereal-time neural signal monitoring component, while the Kinematic DataStream is the data flow from the real-time monitoring componentdetecting the movements of the subject.

In an embodiment of the disclosure, the program contains three parallelthreads. One thread acquires epidural stimulation parameters specifiedthrough a graphic user interface in a programming language, for examplein C++. The second thread contains a loop which continuously updates theinformation about marker positions in space. In the third thread, thecontroller is implemented. In an exemplary embodiment of the disclosure,the program on the signal processing device works as follows. Oncestance detection occurs, a trigger is sent to the program code via, forexample, an ActiveX interface. Inside the bioamp processor runs a realtime cycle based program at a selected frequency, for example 24 kHzcycle frequency. The program continuously evaluates the occurrence of anexternal trigger and then transforms it to an epidural stimulationsignal. For this purpose, stimulation parameters from the C++ graphicuser interface are acquired by the program code. Once the stimulationsignal is generated, it is transmitted to an external stimulus isolator35. The stimulus isolator generates the current pulse from an attachedbattery (not shown), for example a 24V, high voltage battery. Thecurrent pulse is then transmitted back to the epidural space of theanimal at selected stimulation sites through a hardwired connection.

The controller part (herein referred to also as “controller”) allowsderiving, at each gait-cycle, the optimal ES and/or EES frequency on thebasis of the desired locomotion feature output 38 (herein also namedreference output). The reference output 38 is entered by the operator(e.g., clinician) based on the desired behavior. The controller willthen tune automatically the stimulation to make sure the observedbehavior matches the reference thanks to the feedforward component 40,and adapt said frequency at each gait-cycle on the base of the obtainedoutput, thanks to the feedback component 42.

The reference output is defined and entered by the operator of thedevice, for example a clinician, on the base of the desired locomotionpattern. The controller then tunes automatically the electricalstimulation in order to obtain a locomotor pattern matching thereference output.

The controller comprises a feedforward component 40 and a feedbackcomponent 42.

The feedforward component is an input-output linear model, which allowsto directly derive the most suited electrical stimulation frequencygiven the desired reference output at each gait-cycle and to minimizecontrol delays.

Said reference output is a locomotion feature (herein also named gaitfeature), for example step height, i.e. the maximum height reached bythe foot during each gait cycle.

The input-output model captures the observed relationships betweenstimulation and gait features. They can then be used to predict andautomatically tune stimulation so as to modulate output behavior.

Said model is constantly updated using adaptive fitting algorithms whichtake into account fatigue and time-varying characteristics of thelocomotor system of the subject. Adaptive fitting algorithms suitablefor the present disclosure are known in the art and can be chosen by theskilled in the art according to its general knowledge. An example ofthis kind of algorithm is the Least Mean Squares (LMS), but othermethods for linear or non-linear regression are equally valid.

In the device of the disclosure, the stimulation frequency applicable bymeans a) is comprised between 5 and 120 Hz, where in some examples it iscomprised between 25 and 95 Hz.

Pulse-width is kept constant at a value comprised between 0.1 and 1.0ms, for example at 0.2 ms. Amplitude is set between 100-300 uA. Actualranges and sub-ranges can vary from subject to subject.

The feedback component (a Proportional Integral (PI) Control part) ofthe controller compensates for modeling errors or unexpecteddisturbances. At each gait-cycle, it calculates the “error” valuebetween the measured output and the desired reference output. On thebasis of the calculated error value, it adjusts the input so as tominimize said error. For example, at the end of a foot strike, themaximum step height is determined and the error with respect to thedesired reference step height is evaluated; then, the new stimulationfrequency is derived.

The new stimulation frequency is calculated by the feedback componentaccording to the following formula 1:F=K _(p) e+K _(I)Σ_(k=0:t) e _(k)  (1)Formula 1: Sum of Proportional and Integral Correction of the PIController

-   Wherein e is the error, K_(p) is the proportional term and K_(I) is    the integral term and F is the calculated stimulation frequency.

Interestingly, this type of controller requires little a prioriknowledge of the system dynamics, and only employs a reduced number ofparameters to be tuned (namely, the proportional term K_(p) and theintegral K_(I) terms). These are adjusted empirically to matchexperimental recordings for the subject using the system. The first termis proportional term, which drives the correction in proportion to theerror. The second term is proportional to both the magnitude and theduration of accumulated errors, and reduces offsets not taken accountedfor by the proportional term. The new stimulation frequencies aredetermined as the sum of a partial correction plus an integralcorrection.

Proportional Integral (PI) controllers are the widest and better knowntypes of controllers for any application (they account for 95% ofcontrollers) therefore they belong to the general knowledge and do notrequire further explanations.

In an embodiment of the disclosure, the feedforward component of thecontroller employs an input-output model which is a single input-singleoutput model (SISO), wherein one stimulation feature is changed tocontrol one gait feature.

In said embodiment, stimulation frequency applied at a single electrodeor simultaneously in different body sites is the single feature (input)which is changed. The selection of the body site, application mode andpositioning of the device depend on the amount of specificity desiredand the amount of available electrodes and this can be made by theskilled person.

The body site(s) where the stimulation is applied can vary according tothe desired specificity. In one embodiment, stimulation is applied usinga multielectrode array (MEA) covering the spinal segments from T12 to S2if one wants to control and promote leg movements, and/or covering thespinal segments from C3 to T2 if one wants to control and promote armmovements.

In this embodiment using the SISO model, when stimulation is applied ondifferent sites, the stimulation parameters, in particular the frequencyof stimulation, are changed simultaneously in all the sites. Forexample, if electrical stimulation is applied to S1 (sacral 1) and L2(lumbar 2) sites, frequency of stimulation, and timing of stimulationchange together in both sites.

In this embodiment, the single output is a locomotion feature. Saidlocomotion feature can be, for example, step height, amount of dragging,amount of extension, maximum ground reaction forces.

In an alternative embodiment of the disclosure, the system of thedisclosure employs as input-output model, a multiple input-single output(MISO) model, wherein multiple stimulation features are controlled toobtain a single desired gait feature (output).

In said embodiment, the single output is the trajectory of the subjectfoot height over time. Said trajectory is parameterized by the followingformula 2:

$\begin{matrix}\begin{matrix}{{{y(t)} = {{\sum\limits_{i = 0}^{5}{w_{i}t^{i}}} + \epsilon}},{t \in \lbrack {0,1} \rbrack}} \\{{\equiv \lbrack {w_{0},{\ldots\mspace{14mu} w_{5}}} \rbrack},{t \in \lbrack {0,1} \rbrack}}\end{matrix} & (2)\end{matrix}$wherein y is the trajectory,

-   t is the time during which the trajectory is achieved,-   w_(i) wherein i is comprised between 1 and any desired degree of    polynomial fitting. In the exemplary formula a polynomial fit of    degree 5 is chosen to have a certain degree of accuracy, and c is    the error.

Said multiple inputs are, for example, stimulation frequencies appliedon different body sites.

Given the effect that biomechanics play on stepping, input description(i.e. the overall information provided as input) can be also increasedto account for body position (kinematics) and kinetics. This means thatthe input is increased by considering as inputs not just stimulationvalues, but also the biomechanical characteristics of the body. Themodel is thus implemented by accounting for angular positions and speedsof limb joint angles, for example the three joint angles in each leg(Hip, Knee and Ankle), along with Ground Reaction Forces (i.e. themaximum forces applied by the body on the ground), and employing saiddata at each gait-cycle to derive the most suitable stimulation giventhe current biomechanical state. If desired, other body points can beadded or considered, such as for example tips of the feet or any otherbody point useful to provide more information on the biomechanicalcharacteristics of the body.

Thanks to the use of the MISO model, at each foot strike the beststimulation strategy can be derived for each input, i.e. for eachelectrode applied in a different body site, so as to generate thedesired foot trajectory (output).

In one embodiment, the real-time monitoring system b) provided above,acquires 3D coordinates of a limb of said subject. Conventional,commercially available or custom-designed systems are suitable for thepresent disclosure. A motion capture system b) can be, for example, agyrometer attached to a foot of a subject or a foot-strike detector, forexample a force-based switch.

In one embodiment, said means a) for applying to a subject an epiduraland/or subdural electrical stimulation with adjustable stimulationparameters values, as above, is an implantable pulse generator, whichcan be connected to different sites of the spinal cord of a subject. Itis able to change stimulation parameters, in particular stimulationfrequency, and turn off and turn on different stimulation sites. Forexample, it can be connected to sacral and lumbar stimulation sites andsaid sites can be alternatively turned on and off during stance andswing, according to the desired output.

In one embodiment, the stimulation applied for locomotion by means a) isphase dependent. This means that specific electrodes are activatedduring specific sub-phases of the gait cycle. In an exemplaryembodiment, the lateral extensor-related (sacral) electrodes areactivated during stance, and lateral flexor-related (upper lumbar)electrodes are activated during swing. When inactive, the amplitude ofthe corresponding electrodes is zero.

Triggering of each electrode can be based on automatic detection of gaitevents, derived by the signal processing device c) using featuredetection algorithms that use external feedback signals (b). Suchalgorithms allow to detect gait events such as foot-strike or toe-off.Through this algorithm the processor c) provides means a), withinformation regarding the turning on or off of specific electrodes, inorder to promote whole limb flexion and whole limb extension to increasemuscle activity of relevant muscles and thus to improve the locomotoroutput.

The suitable triggering times are obtained for each electrode throughfeature detection algorithms monitoring foot kinematics and derivingoptimal gait events based on the timing within the gait cycle as definedboth by kinematic states and muscular activation.

In one embodiment for locomotion, sacral electrodes are activated(turned on) at footstrike and turned off after the beginning of swing(i.e. after the activation of the tibialis anterior muscle) while lumbarelectrodes are activated before the beginning of swing (i.e. beforeactivation of the tibialis anterior muscle) and turned off beforefoot-strike. This delay is related to the information processing time inthe spinal cord, and transfer of the activating neural command throughmotor nerves to muscles.

In an alternative embodiment, the stimulation applied by means a) is aburst stimulation.

For burst stimulation it is intended that each electrode is activatedfor a certain time (“burst”), wherein the activation times of eachelectrode and the duration of each activation is pre-defined by a user,said user being a clinician or a physiotherapist, for example.

In an embodiment of the present disclosure, and referring to FIG. 13, itis advantageous to provide location-specific stimulation with amulti-electrode array. According to this embodiment, stimulation istriggered for each electrode based on the sub-phase of the gait cycle(lateral sacral electrode during stance, lateral lumbar electrodesduring swing). This time- and location-dependent stimulation results inenhanced whole-limb extension and increased whole-limb flexion, asobserved through kinematic end-point trajectories. The activity of thekey muscles responsible for these movements is increased manifold.

The closed-loop control system object of the disclosure can be used forfacilitating locomotor functions in a subject suffering from injuredlocomotor system, especially due to neuromotor impairment, in particularin a subject suffering from partial or total paralysis of limbs.

Therefore, it is an object of the disclosure the use of said system forfacilitating locomotor functions in a subject suffering from aneuromotor impairment.

In particular, said neuromotor impairment can be partial or totalparalysis of limbs.

Said neuromotor impairment may have been caused by a spinal cord injury,Parkinson's disease (PD), an ischemic injury resulting from a stroke, ora neuromotor disease as, for example, Amyotrophic Lateral Sclerosis(ALS) or Multiple Sclerosis (MS).

In one example, the device is used for facilitating locomotor functionsin a subject after spinal cord injury, Parkinson's disease (PD) orstroke.

In particular, the use of the device of the disclosure allows themaintenance over time of stepping heights, thus reducing the fatigue ofthe subject.

Another advantage of the use of the system of the disclosure is theimprovement of consistency of walking in a subject with a neuromotorimpairment, wherein for consistency of walking is intended the amount ofrepeatable steps with similar locomotor features performed by thesubject.

In a particular application, the system of the disclosure is used tohelp the subject in overcoming an obstacle. For example, it is used forhelping the subject in climbing stairs. In this application, the desiredoutput in the system is the step height required for overcoming anobstacle of a determined height.

In one embodiment of the present disclosure, the system of thedisclosure can be used in combination with a pharmacological treatmentfor further facilitating locomotor functions. In particular, thecombination of the system with pharmacological treatment provides for asynergistic effect on locomotor functions. In particular, apharmaceutical composition comprising at least one agonist tomonoaminergic receptors, in particular to serotoninergic, dopaminergicand adrenergic receptors, can be administered to the subject.

In a further embodiment of the disclosure the system of the disclosureis used in combination with a support system 46. Said support system(apparatus) can be, for example, a treadmill or a robot-assistedbody-weight support or a multidirectional trunk support system.

Generally, in the rehabilitation process a subject can start with thesystem comprising the epidural and/or subdural electrical stimulation ofthe present disclosure and the trunk support, and in a subsequent stepuse only the electrical stimulation.

In one embodiment, said support system is a robotic interface capable ofevaluating, enabling and training motor pattern generation and balancein subjects with neuromotor impairments. For a description of saidrobotic interface reference can be made to the paper “Versatile roboticinterface to evaluate, enable and train locomotion and balance afterneuromotor disorders.” Dominici N, Keller U, Vallery H, Friedli L, vanden Brand R, Starkey M L, Musienko P, Riener R, Nat Med. 2012 July;18(7):1142-7. doi: 10.1038/nm.2845. Further reference can also be madeto the paper of van den Brand R, Heutschi J, Barraud Q, 35 DiGiovanna J,Bartholdi K, Huerlimann M, Friedli L, Vollenweider I, Moraud E M, DuisS, Dominici N, Micera S, Musienko P, Courtine G, “Restoring voluntarycontrol of locomotion after paralyzing spinal cord injury”, Science,2012 Jun. 1; 336(6085):1182-5.

Therefore, it is also an object of the present disclosure a system forrestoring voluntary control of locomotion in a subject suffering from aneuromotor impairment comprising the closed-loop system for real-timecontrol of epidural and/or subdural electrical stimulation as describedabove and further comprising an apparatus selected from the groupconsisting of a treadmill or a robot-assisted body-weight support or amultidirectional trunk support system.

It is also an object of the present disclosure, a method for determiningoptimal stimulation parameters for a subject suffering from a neuromotorimpairment and undergoing a process for facilitating locomotor functionscomprising the following steps:

-   a) determining a first electrical stimulation which has been applied    to said subject bearing means for applying an epidural and/or    subdural electrical stimulation with adjustable stimulation    parameters;-   b) acquiring feedback signals from said subject, said signals being    neural signals and/or providing features of motion of said subject,    through a real-time monitoring system, while this first stimulation    occurs;-   c) transmitting said feedback signals to a signal processing device;-   d) calculating by means of said signal processing device operating a    Real Time Automatic Control Algorithm new stimulation parameters;-   e) providing instructions to said means of step a) for applying a    second epidural and/or subdural electrical stimulation so that said    means are capable to administer a second electrical stimulation with    said new stimulation parameters calculated in step d) to said    subject.

In some examples, such a method may further include indicating ordetermining that the second electrical stimulation comprising said newstimulation parameters are sufficiently optimized such that said secondelectrical stimulation may be repeatedly applied to the subject, forexample, for a duration of a physical exercise. In some examples, therepeatedly applied stimulation may be delivered at a determined pacecorresponding to a desired period of the facilitated locomotor function.As discussed above, in some examples, responsive to said new stimulationparameters being sufficiently optimized, the second electricalstimulation may be applied in an open-loop fashion where feedbacksignals from the subject are either not acquired, or if acquired, do notimpact the second electrical stimulation. The second electricalstimulation may be provided for the desired duration, and may bedelivered at a determined pace (e.g. a pace that avoids fatigueassociated with stimulation and/or a pace that is optimal for thedesired physical exercise).

In the above-described method, it is disclosed that the subject bearsthe means for applying an epidural and/or subdural electricalstimulation (or means for neuromodulation). Thus, it may be understoodthat such means may include a fully implantable neuromodulator,partially implantable neuromodulator, or external neuromodulator, asdiscussed in further detail below with regard to FIG. 18.

An optimal stimulation pattern (said pattern being determined by theensemble of said stimulation parameters) is the pattern which allowsobtaining at each gait cycle the desired reference output. Said optimalstimulation pattern is calculated by the signal processing device instep d) thanks to the Real Time Automatic control Algorithm, as abovedescribed. In one embodiment, the reference output is a step height of alimb of the subject defined by the operator of the method and theoptimal stimulation pattern is the one which allows the obtainment ofsaid step height. In another embodiment, the reference output is thetrajectory of the subject foot height over time and the optimalstimulation pattern is the one which allows the obtainment of saidtrajectory.

In one embodiment, in step b) 3D coordinates from said subject areacquired, for example coordinates of one or more of hip, knee, ankle andfoot. In one example, such coordinates includes acquisition of stepheight.

Such configuration provides the maximum strength to stepping in terms ofground reaction forces, muscle activation and support of body weight,while minimizing coactivation.

In another embodiment, in step b) cortical signals from the sensory,motor, sensorimotor or pre-motor cortex are acquired as feedbacksignals.

In one embodiment, adaptive fitting algorithms are used, for exampleadaptive fitting algorithms that take into account fatigue andtime-varying characteristics of the locomotor system.

Generally, said first electrical stimulation has a frequency comprisedbetween 5 and 120 Hz, more specifically it is comprised between 25 and95 Hz.

In one embodiment, said Automatic Control Algorithm in step d) comprisesa feedback component and a feedforward component.

Said feedback component compensates for modeling errors or unexpecteddisturbances, as explained above for the system used to carry out thismethod.

In one embodiment, said stimulation pattern of step e) comprises asecond stimulation frequency which is calculated by said feedbackcomponent according to the formula 1 above.

In one embodiment, said feedforward component employs an input-outputmodel which is a single input-single output model (SISO), wherein onestimulation feature is changed to control one gait feature, or,alternatively a multiple input-single output (MISO) model, whereinmultiple stimulation features are controlled to obtain a single desiredgait feature (output), or, alternatively a multiple input-multipleoutput (MIMO) model, wherein multiple stimulation features arecontrolled to obtain multiple desired gait features (output). For adetailed explanation, see above in connection with the system of thepresent disclosure.

Another object of the present disclosure is a method for facilitatingstanding and walking functions in a subject suffering from neuromotorimpairment comprising the following steps:

-   a) using a system for restoring voluntary control of locomotion    comprising the closed-loop system as above described;-   b) providing to said subject a first epidural and/or subdural    electrical stimulation with adjustable stimulation parameters;-   c) acquiring feedback signals from said subject, said signals being    neural signals and/or providing features of motion of said subject;-   d) transmitting said feedback signals to a signal processing device;-   e) calculating by means of said signal processing device operating a    Real Time Automatic Control Algorithm new electrical stimulation    parameters;-   f) providing to said subject a second electrical stimulation with    said new electrical stimulation parameters calculated in step e),    and optionally-   g) administering to said subject before and/or during administration    of said first and/or said second electrical stimulations a    pharmaceutical composition comprising at least one agonist to    monoaminergic receptors.

In one embodiment, in step e) said Real Time Automatic Control Algorithmcomprises a feedforward component employing an input-output model whichis a single input-single output model (SISO), wherein one stimulationparameter is changed to control one gait feature, or, alternatively amultiple input-single output (MISO) model, wherein multiple stimulationparameters are adjusted to obtain a single desired gait feature(output), or, alternatively a multiple input-multiple output (MIMO)model, wherein multiple stimulation features are controlled to obtainmultiple desired gait features (output).

In particular, the combination of controlled epidural and/or subduralelectrical stimulation applied using the closed-loop system of thedisclosure with a robotic training and optionally also with apharmacological stimulation (not shown) allows for the restoring ofvoluntary control of locomotion.

In one embodiment, said acquisition of feedback signals of step c),comprises acquisition of the coordinates of one or more of hip, knee,ankle and foot. In one example, such coordinates includes acquisition ofstep height.

In another embodiment, said feedback signals acquired in step c) arecortical signals from sensory, motor, sensorimotor or pre-motor cortex.

Generally, said first electrical stimulation has a frequency comprisedbetween 5 and 120 Hz, more specifically it is comprised between 25 and95 Hz.

Turning now to FIG. 18, a block diagram of an example system 100 forfacilitating and restoring locomotion in subjects, according to anembodiment of the present disclosure, is shown, and herein explained. Itmay be understood that example system 100 is substantially equivalent tothe system described above at FIG. 14, and that the block diagram ofFIG. 18 is meant to convey relevant aspects of FIG. 14. The system 100may include a subject 101 (e.g. 10), to which epidural and/or subduralelectrical stimulation is applied. For example, epidural and/or subduralelectrical stimulation may be provided via a means for electricalstimulation 105 (e.g. 15), also referred to herein as a means forneuromodulation, with adjustable stimulation parameters. As an example,the means for electrical stimulation 105 may include one or moreelectrodes, an electrode array, an implantable pulse generator, etc. Themeans for electrical stimulation 105 may be utilized to apply epiduraland/or subdural electrical stimulation to any stimulation site along thespinal cord of the subject 101. For example, stimulation sites arelumbar and sacral sites for lower limb stimulation and cervical sitesfor upper-limb stimulation. Lower limb stimulation may be applied, forexample, for facilitating standing and walking in a subject, whileupper-limb stimulation is applied, for example for facilitating reachingand grasping. In one example of the disclosure, said stimulation sitesare at least two, and each stimulation site may be independently turnedon or off.

In some examples, the means for neuromodulation may comprise an externalneuromodulator, which may be applied external to the subject. Forexample, the external neuromodulator may comprise a device wherebystimulation is delivered to a desired stimulation site (e.g. along thespinal cord of the subject), via hydrogel electrodes. In other examples,the means for neuromodulation may comprise a partially implantedneuromodulator. In still other examples, discussed above, the means forneuromodulation may be fully implantable to the subject. In the case ofthe fully implantable means for neuromodulation, epidural and/orsubdural stimulation may be regulated by a wireless controller, forexample. Similarly, stimulation via the external neuromodulator and/orpartially implated neuromodulator may be regulated via a wirelesscontroller.

In one example for facilitating locomotion, the stimulation applied bythe means for electrical stimulation 105 may be phase-dependent, wherespecific electrode(s) may be activated during specific sub-phases of thegait cycle. For example, the lateral extensor-related (sacral)electrodes may be activated during stance, while the lateralflexor-related (upper lumbar) electrodes may be activated during swing.When the subject 101 is inactive, it may be understood that an amplitudeof electrode(s) is zero. Accordingly, it may be understood that themeans for electrical stimulation 105 as applied to sacral and lumbarsites may be alternatively activated to promote, respectively,whole-limb extension or flexion.

In some examples, said means for electrical stimulation 105 may providea burst stimulation. In such an example, it may be understood that eachelectrode (or each multi-electrode array) may be activated for a certaintime (“burst”), wherein the activation times of each electrode (ormulti-electrode array), and the duration of each activation ispre-defined by a user 170, said user 170 being a clinician orphysiotherapist, for example.

In an example, electrical stimulation provided via the means forelectrical stimulation 105 may be location-specific, wherein stimulationparameters (e.g. waveform, amplitude, pulse width, frequency) of eachindividual electrode may be modified in real-time.

In yet another example, electrical stimulation provided via the meansfor electrical stimulation 105 may be time-specific, where each singleelectrode may be individually turned ON and OFF in real time based onexternal trigger signals. In still another example, electricalstimulation provided via the means for electrical stimulation 105 may befrequency-specific. For example the means for electrical stimulation 105may provide a stimulation frequency comprised of between 5 and 120 Hz,more specifically between 25 and 95 Hz, wherein the resolution is, forexample, of 1 Hz.

The subject 101, to which epidural and/or subdural electricalstimulation is applied may be continuously monitored in real-time via areal-time monitoring system/real-time monitoring component 102 (e.g.20). The real-time monitoring system may include a motion capture systemor an accelerometer or any other equivalent means. In one example, oneor more limb position markers or limb position sensors 106 (e.g. 24),may be placed on limb(s) of the subject 101, and which may be visualizedby the real-time monitoring component 102 in such a way that thereal-time monitoring system 102 acquires 3D coordinates of limb movementwhen stimulation provided via the means for electrical stimulation 105is applied. Position markers 106 may be of a reflective type, meaningfor example that they reflect infrared light emitted by camerascomprising the real-time monitoring components 102, thus allowing thetracking of limb movements by subject 101. However, position markers maybe of other types as well. Examples of other position markers or sensors106 may include optical systems, electromagnetic systems, ultrasonicsystems, and combinations of systems suitably integrated by what isknown as the “sensor fusion” method, including a triangulation systemusing radio frequency antennae and an inertial sensor. The location orposition of position markers 106 may be acquired in real-time and may beassociated with specific labels (for example crest, hip, knee, ankle,foot) according to user-defined kinematic model 120, where saidkinematic model may be built on a per-subject basis for high accuracytracking of the position markers 106 by the real-time monitoringcomponent 102. Said kinematic model 120 may evaluate a set of ruleswhich may compare X, Y, and Z coordinates of each marker, and may derivewhich set of coordinates corresponds to which label (e.g. crest, hip,knee, ankle, foot). Said kinematic model 120 thus may match 3D positionsof position markers 106 with the joint they are attached to.

The set of rules of kinematic model 120 may operate in three steps. In afirst step, the kinematic model 120 may evaluate mediolateralcoordinates of position markers 106 to distinguish between those relatedto a right and/or left limb of subject 101, thus identifying twosubsets. In a second step, for each one of these two subsets, top-downrules may distinguish using the vertical coordinates, for example crest(highest position marker), the couple hip/knee (lower than crestposition markers) and the couple ankle/foot (lowest two positionmarkers). Finally, in a third step, for each one of these two couples,forward coordinates may help distinguish knee from hip (knee is moreforward than hip), and foot from ankle (foot is more forward thanankle).

In one example, said real-time monitoring components 102 may comprise aVicon Kinematic System (Nexus), however other commercially available orcustom-built systems are also suitable for the present disclosure.

After acquiring coordinates based on the position markers 106, theacquired coordinates may then be transmitted to an external signalprocessing device 150 (e.g. 30) Such a transmission may comprise aKinematic Data Stream 125 (e.g. 22). Thus, it may be understood that theKinematic Data Stream 125 may comprise data flow from the real-timemonitoring component 102 detecting movements of subject 101.

In some examples, Kinetic Data stream 125 may additionall oralternatively include ground reaction forces, where ground reactionforces may include a maximum force applied via the subject on the groundduring gait, for example.

In another example, the real-time monitoring component 102 may acquireneural signals from subject 101 via neural sensors 107. Said neuralsignals (e.g. 32) may provide information about the locomotor state andthe neuronal activity of subject 101 to real-time monitoring component102, and which may then be transmitted to signal processing device 150.As an example, neural signals may provide information related to thegait cycle of subject 101, and may be used to control or refine in realtime the triggering of the means for electrical stimulation 105,respectively substituting or co-operating with the kinematic-feedbackalgorithms described above.

In an example, said neural sensors may include electrode arraysimplanted in the limb area of the sensorimotor cortex of subject 101,and may collect information about a locomotor intention of subject 101.Using machine-learning approaches 121, such information may be decodedand discriminated into two behavioral states, “rest” or “walk”. Thedecoded information may then be transmitted to signal processing device150, which may switch ON or OFF a feedback-feedforward controller 180,such that a desired locomotor pattern may be achieved. The decodedinformation transmitted to signal processing device 150 may comprise aNeural Data Stream 126. Thus, it may be understood that the Neural DataStream may comprise a data flow from the real-time monitoring component102, corresponding to data from neural sensors 107.

On the external signal processing device 150, a program 153 comprisingan automatic control algorithm may interface simultaneously with theKinematic Data Stream 125 and/or the Neural Data Stream 126 (e.g. datafrom the real-time monitoring components 102), and the means forelectrical stimulation 105. The program 153 comprising the automaticcontrol algorithm may be in any programming language able to operate inreal time; for example it can be in C, C++, C#, Simulink/xPC, etc.Program 153 comprising the automatic control algorithm may be compiledaccording to the general knowledge of those skilled in the art usingcustom-made or commercially available software, for example TDT. Saidprogram 153 comprising automatic control algorithm may be programmed todetect foot-strike in real time and to adapt the electrical stimulationat each gait-cycle.

In an example, program 153 may contain three parallel threads, termedfirst thread 155, second thread 156, and third thread 157. First thread155 may acquire epidural and/or subdural stimulation parametersspecified through a graphical user interface in a programming language,for example in C++. The second thread 156 may comprise a loop which maycontinuously update information about the position of position markers106 in space. The third thread 157 a controller is implemented. In anexample, the program 153 works as follows. Once stance detection occursvia the real-time monitoring component 102, a trigger is sent to theprogram 153 via, for example, an ActiveX interface. Inside the signalprocessing device 150 (e.g. bioamp processor) runs a real-time cyclebased program 153 at a selected frequency, for example 24 kHz cyclefrequency. Program 153 may continuously evaluate the occurrence of anexternal trigger, and then transforms it into an epidural and/orsubdural stimulation signal. For such a purpose, stimulation parametersfrom the C++ graphic user interface are acquired by the program 153code. Once the stimulation signal is generated, it is transmitted to anexternal stimulus isolator 130 (e.g. 35). The external stimulus isolator130 generates a current pulse from an attached battery 131, for examplea 24V high voltage battery. The current pulse may then be transmitted tothe epidural and/or subdural space of the subject 101 at selectedstimulation sies through a hardwired connection.

The controller part (herein referred to as “controller”) allowsderiving, at each gait-cycle, an optimal electrical stimulation and/orepidural electrical stimulation frequency on the basis of a desiredlocomotion feature output 181 (herein also named a reference output 181)(e.g. 38). The reference output 181 may be entered by user 170, based ona desired behavior. The controller may then tune automatically theelectrical stimulation to make sure an observed behavior matches thereference output 181. More specifically, program 153 may comprise afeedforward component 160 (e.g. 40), and a feedback component 161 (e.g.42). Said feedforward component 160 may comprise an input-output linearmodel, which may allow to directly derive the most suitable electricalstimulation frequency given the desired reference output 181 at eachgait-cycle, and to minimize control delays. Said reference output 181may comprise a locomotion feature, also termed gait feature herein. Saidgait feature may include step height, for example, a maximum heightreached by a foot of subject 101 during each gait cycle.

Feedforward component 160 (e.g. input-output model) may thus captureobserved relationships between stimulation and gait features. Suchinformation may then be utilized to predict and automatically tunestimulation so as to modulate output behavior.

Feedforward component 160 may be constantly updated using adaptivefitting algorithms known in the art (e.g. Least Mean Squares or othermethods for linear or non-linear regression) which may take into accountfatigue and time-varying characteristics of the locomotor system ofsubject 101.

The feedback component 161 (a Proportional Integral Control part) of thecontroller compensates for modeling errors or unexpected disturbances.At each gait-cycle, feedback component 161 may calculate an “error”value between a measured output and desired, or reference, output 181.On the basis of the calculated error value, the feedback component 161may adjust the input so as to minimize said error. For example, at theend of a foot strike, a maximum step height may be determined and theerror with respect to the desired reference step height may beevaluated, then, a new stimulation frequency may be derived.

In some examples, the system 100 of the disclosure may include a supportsystem 115 (e.g. 46). For example, the system 100 of the disclosure maybe used in combination with support system 115. Support system 115 maybe a treadmill, a robot-assisted body-weight support, or amultidirectional truck support system, for example. In a rehabilitationprocess, subject 101 may in some examples start with system 100comprising epidural and/or subdural electrical stimulation incombination with support system 115, and may in a subsequent step useonly the means for electrical stimulation 105. In yet another example,the system 100 of the disclosure including subject 101 may include ameans for pharmacological stimulation 109. For example, pharmacologicalstimulation, or treatment, may be provided to subject 101 via meansknown to those in the art. For example, means for pharmacologicalstimulation 109 may include injection via a needle, where said injectionis controllable via signal processing device 150. However, other meansfor pharmaceutical stimulation may include substances taken orally,applied to the skin (e.g. topical), transmucosal application,inhalation, etc. In one example, the system 100 of the disclosure may beused in combination with a pharmacological treatment via the means forpharmacological stimulation 109, for further facilitating locomotorfunctions. In particular a pharmaceutical composition comprising atleast one agonist to monoaminergic receptors, for example serotonergic,dopaminergic, and adrenergic receptors, may be administered to subject101. The disclosure will be further described by means of examples.

In one example, such a system may comprise a system for determiningoptimal stimulation parameters for a subject (e.g. 101) suffering from aneuromotor impairment, where the subject is undergoing a process forfacilitating locomotor functions. Such a system may thus include a meansfor neuromodulation (e.g. 105) for applying electrical stimulation withadjustable stimulation parameters, one or more sensors (e.g. limbposition sensors 106, neural sensors 107, and/or ground reactionforces), and a real-time monitoring system (e.g. 102). Such a system mayfurther include a signal processing device, operating an automaticcontrol algorithm that interfaces simultaneously with a data flow fromthe real-time monitoring system and the means for neuromodulation. Thesignal processing device may acquire feedback signals from the real-timemonitoring system, where the real-time monitoring system receivessignals from the sensors, where the signals include neural signalsand/or signals providing features of motion of the subject. The signalprocessing device may thus calculate stimulation parameters via theautomatic control algorithm, and may provide instructions to the meansfor neuromodulation for applying an electrical stimulation withcalculated parameters to the subject.

In such a system, the calculated stimulation parameters may in someexamples comprise optimized stimulation parameters, such that thecalculated stimulation parameters may be applied in an open-loopfashion, as discussed above. The calculated stimulation parameters maybe provided for a desired duration, and may be delivered at a determinedpace. In some examples, the determined pace may comprise a pace that isa function of avoiding fatigue that may otherwise result if the pace ofdelivery were quicker (e.g. delivered more frequently), or deliveredcontinuously.

In such a system, the means for neuromodulation may include a fullyimplantable neuromodulator, partially implantable neuromodulator, orexternal neuromodulator, as discussed in further detail above withregard to FIG. 18.

In such a system, the stimulation parameters may be selected from thegroup consisting of waveform, amplitude, pulse width, and frequency, forexample. In one example, the electrical stimulation may comprise afrequency between 5 Hz and 120 Hz, and the means for neuromodulation maybe configured to provide phasic stimulation or burst stimulation. Insome examples, the means for neuromodulation may be configured to applystimulation to at least two stimulation sites, where each stimulationsite may be independently turned on or off. As an example, the means forneuromodulation may comprise one or more electrodes, where theelectrical stimulation is time-specific (e.g. burst stimulation), andwhere each of the one or more electrodes may be individually turned ONand OFF in real time based on external trigger signals (e.g. data fromlimb position sensors and/or neural sensors).

In some examples, the automatic control algorithm may comprise a realtime automatic control algorithm. In other words, the system maycontinuously acquire data, process the data, and update stimulationparameters for the duration of a particular physical exercise, forexample. In one example, the real time automatic control algorithm maycomprise a feedforward component employing one of a single input-singleoutput (SISO) model, or a multiple input-single output (MISO) model, ora multiple input-multiple output (MIMO) model, where the SISO modelcomprises changing one electrical stimulation device stimulationparameter to control one gait feature of the subject, and where the MISOmodel comprises adjusting multiple electrical stimulation devicestimulation parameters to obtain a single gait feature of the subject,and where the MIMO model comprises adjusting multiple electricalstimulation device stimulation parameters to obtain a multiple gaitfeatures of the subject.

In one example of such a system, the electrical stimulation may compriselower limb stimulation of the subject. For example, the lower limbstimulation may comprise at least one of lumbar and/or sacral sites.More specifically, the means for neuromodulation (e.g. epidural and/orsubdural electrical stimulation device) may comprise electrodes appliedon the sacral and lumbar sites, and where the electrodes may bealternatively activated to promote, respectively, whole-limb extensionor flexion of the subject.

In another example of such a system, the electrical stimulation maycomprise upper limb stimulation. In such an example, the electricalstimulation may be applied to the subject on at least one cervical site.More specifically, the means for neuromodulation (e.g. epidural and/orsubdural electrical stimulation device) may comprise electrodes appliedon at least one cervical site, where the electrodes are alternativelyactivated to facilitate reaching and grasping.

The signal processing device of such a system may in some examplesautomatically detect gait events of the subject, and may define specificsub-phases of gait of the subject during locomotion.

In such a system, the signals providing features of motion of thesubject may comprise one or more of coordinates, kinetic information,acceleration, speed, angle, angular speed, and/or angular accelerationof one or more of hip, knee, ankle, foot, shoulder, elbow, hand, wrist,and/or digits of the subject.

In such a system, the neural signals may include cortical signals fromone or more of a sensory, motor, sensorimotor, and/or pre-motor cortex.

In such a system, the electrical stimulation may be repeatedly appliedto the subject during the process for facilitating locomotor function,and may in some examples be delivered at a predetermined pacecorresponding to a desired period of the process for facilitatinglocomotor functions.

Discussed herein, in some examples, providing instructions to theepidural and/or subdural electrical stimulation device (or means forneuromodulation) may comprise providing instructions automatically. Inone example, providing instructions automatically may include providinginstructions with little or no human control. In another example,providing instructions automatically may include providing instructionswith no human control. For example, providing instructions automaticallymay comprise a condition where the signal processing device may acquirefeedback signals from the real-time monitoring system, and the signalprocessing device may calculate stimulation parameters via the automaticcontrol algorithm, and based on the calculated stimulation parameters,instructions may be provided automatically, without human control, tothe epidural and/or subdural electrical stimulation device.

In another example, providing instructions to the epidural and/orsubdural electrical stimulation device (or means for neuromodulation)may comprise providing instructions semi-automatically. For example,providing instructions semi-automatically may comprise a condition wherethe signal processing device may acquire feedback signals from thereal-time monitoring system, and the signal processing device maycalculate stimulation parameters via the automatic control algorithm,but where the calculated stimulation parameters may be modified oradjusted via human or operator intervention. For example, suchmodification may be the result of the operator noting the feedbacksignals from the real-time monitoring system, and determining to adjustor modify one or more parameters comprising the calculated stimulationparameters, prior to the instructions being provided to the means forneuromodulation for applying an electrical stimulation to the subject.More specifically, it may be understood that one or more of theparameters that may be calculated via the signal processing device, maybe independently modified via an operator, if so desired. Thus,semi-automatically may include any situation where some combination ofcalculated stimulation parameters, and operator-manipulated stimulationparameters, are provided to the means for neuromodulation for applyingelectrical stimulation to the subject.

In some examples, providing instructions to the epidural and/or subduralstimulation device (or means for neuromodulation) may include providingthe instructions online. In such an example, it may be understood thatproviding the instructions online may include providing the instructionsdirectly. As an example, the means for neuromodulation, or the epiduraland/or subdural stimulation device, may be controlled directly, oronline, via the signal processing device, responsive to the signalprocessing device calculating the stimulation parameters. For example,providing instruction online may refer to the means for neuromodulationbeing under direct control of the signal processing device with which itis associated. However, in other examples, the means forneuromodulation, or the epidural and/or subdural stimulation device maybe controlled indirectly, or offline, responsive to the signalprocessing device calculating the stimulation parameters.

Furthermore, discussed herein, “real-time” may comprise no delay, or aslight or minimum delay in hardware or software producing a systemresponse to commands or responsive to feedback. For example, the delaymay not be larger than 30 milliseconds. The delay may be in a rangebetween 0.0001 milliseconds and 10 milliseconds. For example, providinginstructions to the means for for neuromodulation (or epidural and/orsubdural stimulation device) may be in real-time, where a delay betweenacquiring feedback signals, calculating stimulation parameters, andproviding instructions to the means for neuromodulation may be less than30 milliseconds, and may preferably be in the range between 0.0001 and10 milliseconds. In another example, referred to above, where the markerpositions of the subject are acquired in real-time and associated tospecific labels, it may be understood that there may be no delay, or aslight or minimum delay in the acquisition of the marker positions, andthe association to specific labels.

EXAMPLES Example 1

Effect of Epidural Electrical Stimulation on Bipedal Stepping

Experimental Procedures

All experimental procedures were approved by the Veterinary Office ofthe Canton of Zurich. The experiments were conducted on 7 adult, femaleLewis rats (˜220 g). The rats were housed individually on a 12 hlight/dark cycle, with access to food and water ad libitum. All spinalcord-injured animals received manual expression of the bladder twice aday and stretching of the hindlimb flexor and adductor muscles once aday to prevent joint contractures. Additionally, all animals were puttogether into a large cage three times a week to meet their socialneeds.

After surgeries (complete midthoracic transection of the spinal cord atlevel T 6/7), two epidural electrodes implanted at spinal segments L2and S1, and two electromyographic (EMG) electrodes chronically implantedon both legs for the TA (Tibialis Anterioris) and MG (Gastrus Medialis)muscles), all rats were allowed to recover for 5 weeks to regain stablelevels of excitability in the spinal networks to facilitate locomotionvia EES (Musienko, P., Heutschi, J., Friedli, L., den Brand, R. V. &Courtine, G. Multi-system neurorehabilitative strategies to restoremotor functions following severe spinal cord injury. Experimentalneurology, doi:10.1016/j.expneurol.2011.08.025 (2011); Keller.Construction and Control of a Multi-Directional Support System forNeurorehabilitation of Spinal Cord Injured Rats. Master Thesis inMechanical Engineering, ETH Zürich (2009)). Treadmill training wasperformed every other day for 30 minutes starting from day 9 postsurgery (P9). EES frequencies during training were kept constant at 40Hz.

Recordings were performed on treadmill (constant speed of 9 cm/s) oroverground (body-weight robotically assisted—FIG. 1). A robust baselineof locomotor output was recorded for at least ten successive consistentgait-cycles enabled by EES. Stimulation at both S1 or L2 were tested fortheir modulatory capacities by changing stimulation frequencies inintervals of 5 Hz ranging from 0 to 120 Hz. Pulse-width was keptconstant at 0.2 ms. Amplitude was set between 100-300 uA depending onthe animal to allow for adequate BWS (Body Weight Support). Theseparameters were kept constant for the entire recording session.

Frequency Modulation of SISO System

In order to perform the system identification process, we excited thesystem with varying stimulation parameters and monitored how locomotorpatterns are affected.

We focused on the effect of frequency, this parameter having beensurprisingly found out as the more effective one. We performed detailedanalyses of the kinematic and EMG outputs. A total of 116 kinematicparameters (or “features”) were evaluated for each gait cycle, as a wayto quantify specific aspects of gait.

We focused at finding relationships in a single-input single-ouput(SISO) configuration, for which the modeling and control process isoften straightforward, and which entail that simple relations betweeninputs and outputs can be directly verified online. Thus both electrodes(S1 and L2) were coupled and their stimulation changed together. Therange of EES frequencies applied during the system identificationprocess ranged from 5 Hz up to a maximum of 120 Hz, after which theconsistency of locomotor outputs clearly decayed (e.g., jumping). Withinthis functional range, we restricted our modeling and further control towork between 25 Hz and 95 Hz. This is because we were particularlyinterested in the modulation of gait parameters for conditions with aminimum percentage of drag and minimal stepping variability.

Single-Input Single-Output (SISO) Mapping

Data Analysis & Feature Selection

In order to find relationships between stimulation and locomotorfeatures, we performed well-known data dimensionality reductiontechniques (Principal Component Analysis) to emphasize correlationsbetween the different features employed to quantify stepping, and toextract specific output parameters that explain a high amount ofvariance in the data (PCs 1-3 explained up to 49% of variability of theentire data set, hence highlighting clear structures in the data,specifically modulated with the input). Indeed different stimulationconditions generated clustered outputs in PC space (FIG. 3), and thisregardless of the inter-animal differences. It thus appears as clearthat EES frequency clearly and consistently modulates locomotor patternsacross animals in unique and predictive ways. Gait features that weremost correlated with changes in EES frequency could be grouped intofunctional clusters of flexion, extension, speed and variability alongPC1.

Foot Height is Linearly Correlated to Stimulation Frequency

The factor loadings revealed that step height (i.e., the maximum heightreached by the foot during each gait cycle) is nicely modulated with theinput. Their relationship happened to be close to linear and step heightis modulated with the input (FIG. 4), which allowed us to build a linearinput-output model to be further used for our controller. This appearedas a particularly well suited model for our control purposes.

Example 2

Closed-Loop Control Based on SISO Models

Closed-Loop Monitoring and Control Setup

A Vicon Kinematic System (Nexus) acquired 3D coordinates of reflectivemarker positions in real time (200 Hz) according to a user-definedkinematic model (markers for: Crest, Hip, Knee, Ankle and Foot, for bothlegs), built on a per-animal basis for high accuracy tracking. Theacquired coordinates were then transmitted through the Vicon Data Streamto an external Personal Computer via an Ethernet port. On the externalPersonal Computer, our C++ program was programmed to interfacesimultaneously with the Kinematic Data Stream and the TDT Program Code(RZ5 Bioamp Processor, TDT). The C++ Code was programmed to detectfoot-strike in real time and adapt the stimulation using the linearmodel previously built (FIG. 5), as mentioned in example 1, thanks tothe presence of a controller. Thus, a closed-loop control structure hasbeen established.

Controller Structure

The input-output linear model was employed as feedfoward componentwithin our closed-loop control structure. This allowed to directlyderive the most suited stimulation frequency given the desired referenceoutput at each gait-cycle, and to minimise control delays. We note thatthe model was also constantly updated using adaptive filteringtechniques (Least Mean Squares—LMS) so as to allow the linearinput-output mapping to account for time-dependencies or inter-animaldifferences.

The control structure was complemented with a feedback component (aProportional Integral (PI) Control part) to compensate for modellingerrors or unexpected disturbances not accounted for in our staticinput-output mapping. The feedback control calculates the “error” valuebetween a measured output variable and a desired reference, and thenattempts to minimize it by adjusting the control inputs.

Control corrections were applied at each foot strike. At that instant,the ‘real time control thread’—programmed in C++—determined the maximumstep height during the previous gait cycle. It evaluated the error withrespect to the desired reference value and derived the new stimulationfrequency. Formula 1 depicts how the new stimulation frequencies werecalculated. The new stimulation frequencies were determined as the sumof a partial correction+an integral correction.

We applied the Proportional Integral Controller in two experimentalsettings: first on treadmill as a proof of concept, and then on a runwayplus stair-case for a more practical-oriented application. In the latterconfiguration, few steps were available for adapting the feed-forwardcomponent; thus, in order to reduce model-based errors, we chose toremove the predictive term (i.e. the feedforward component) and let thefeedback loop drive the controller. The correction of stimulationfrequencies was consequently based only on the proportional and integralterms of the PI Controller.

Controllability and Model Accuracy

In order to quantify the quality of the usability of our model forclosed-loop control, and to verify its accuracy and adaptability overtime, we designed specific control tasks to test how the system wouldbehave when pushing the limits of the system.

The first test involved constant changes in the desired reference(changes happened at every gait cycle) in order to compel the controllerto employ the feed-forward model and hence quantify its accuracy, andits suitability even in situations where the system is pushed to thelimit. Together with this, we could analyze how big (and how fast)changes could be applied to the reference with the system following thedesired behaviour.

The second test looked at the fine-tuning, and was specificallyconceived to evaluate whether the controller could help reducing theinherent variability in bipedal stepping. This was carried by imposing aconstant reference, and by narrowing the beam allowed for the stepping.

More details are presented in the next sections.

Evaluation of Feedforward Component—‘Scissor Task’

We applied constant changes of reference at constant rates, asking thestep-height to constantly increase or decrease following a periodicreference tracking with a triangle waveform Different changing rateswere tested to try and quantify the degradation in accuracy as the speedincreases.

This task is important because it is mainly driven by the feed-forwardcomponent (i.e., the linear model), and it thus allows to quantify towhat extent the input-output relationship captures the system response.It further evaluates the behaviour of the system under limit conditions,in which the step-heights need to be adapted at fast changing rates.FIG. 6 underlines the astonishing accuracy of the model, no matter howfast the system was required to change (No statistical difference wasfound between the errors under the different conditions).

Evaluation of Feedback Component—Fine Tune of Stepping Variability

Our second experimental condition was a “constant reference” task, inwhich the step-height was required to maintain a constant level over 48consecutive steps. This framework, mostly driven by the feedbackcomponent, focuses on whether our controller also allows reducing theinherent variability in stepping, i.e. to fine-tune the output.

For this matter, the beam (within which variability is allowed) wasconstantly reduced, thereby forcing the controller to try and correctfor more steps that fall outside of the allowed range (FIG. 7 shows anexample of the beam being reduced from +/−5 mm to +/−1 mm each 48gait-cycles).

FIG. 8 outlines the statistical results for healthy (black),non-controlled (white) and controlled (grey—different beams indicated inthe x-axis label, from +/−1 mm to +/−8 mm). Big values for the beam(e.g., +/−8 mm) resulted in a variability in the range of what isobserved in non-controlled animals (since no steps fall outside of thebeam and thus no control is actually acting). The variability isslightly reduced after +/−6 mm, and reaches a plateau that is kept evenwhen trying to reduce the beam.

Example 3

Functional Applications

Fighting Fatigue for Improved Rehabilitation.

We then tested the capacity of the presented model and controller to beemployed for applications that might prove useful form a rehabilitationperspective.

One of the major motivations that underlie controlled electricalstimulation, either when referring to EES or FES (Functional ElectricalStimulation), is that it may help to compensate for the fatigue thatoften derives from an external source of muscle activity. In theframework of EES-induced locomotion, fatigue yields a decreased flexionand extension pattern during stepping over time, and hence inducinglower stepping and eventually collapse.

Here we tested whether the present controller could be employed toensure that consistent stepping heights are maintained over time, as away to fight fatigue. We quantified the duration of good stepping inboth controlled and non-controlled (constant 40 Hz) trials, and we show(FIG. 9) that time of good stepping allowed by the controller wasincreased 5-fold with respect to the non-controlled situation. The fulllength of trials were also extended almost two times (reported resultsare on n=3 animals).

Climbing Stairs

Our second application, which appeared as a natural step forward in theevaluation of our controller, was to quantify its applicability forclimbing stairs. Animals were walking overground, and were asked toclimb stair-cases of 3 different heights (small 13 mm, medium 20 mm andhigh 30 mm) as a way to accurately quantify our capacity to control themodulation appropriately depending on the requirement of the situation,i.e., the controller was set to precisely adapt the animal stepping soas to overcome the obstacle at 30 mm above the stair (FIG. 10).Interestingly, the force exerted on the force-place also shows markedmodulation as frequency is increased.

Example 4

MIMO Models for Exploiting Multi-Site Stimulation

The feedback controller aforementioned has pointed out the capacity tocontrol one feature of gait by changing one characteristic ofstimulation (frequency at both S1 and L2 together). It is however ourcontention that the spinal cord is composed of widely distributed, yethighly synergistic neural circuits that can generate a variety ofmovements when recruited in a task-specific manner.

Time-Encoded Output & Biomechanical States

Extending the previous feedback controller to multi-site stimulation (ina ‘multiple-input single-output’ (MISO) approach), requires controllingeach single site independently based on a measure of error in theoutput. The main challenge is to determine how to update each individualelectrode based on a single error value. Interestingly, depending ontheir location, certain epidural electrodes affect motor output mostlyduring ‘flexion’ (at the beginning of swing), whereas others are mostlyresponsible for ‘extension’ (during the end of swing and stance). Theoutput however will be considered over the whole swing phase, i.e. thetrajectory of the foot height over time. This trajectory may beparameterized (for instance, through a polynomial fitting of degree N:

$\begin{matrix}{{{y(t)} = {{\sum\limits_{i = 0}^{5}{w_{i}t^{i}}} + \epsilon}},{t \in \lbrack {0,1} \rbrack}} \\{{\equiv \lbrack {w_{0},{\ldots\mspace{14mu} w_{5}}} \rbrack},{t \in \lbrack {0,1} \rbrack}}\end{matrix}$

Which captures time-information in a few parameters w_(i) to which eachstimulation input contributes (FIG. 11 a—left).

In addition given the effect that biomechanics play on stepping, weincreased our input description to account for body position(kinematics—FIG. 11b , right) and kinetics: we accounted for angularpositions and speeds of the three joint angles in each leg (Hip, Kneeand Ankle) along with Ground Reaction Forces in our model, and employthem at each gait-cycle to derive the most suitable stimulation giventhe current biomechanical state.

At each foot-strike, the built model then allows to derive the beststimulation strategy for each electrode independently, so as to generatein the output a foot trajectory that is closer to what is desired. Themodel was built on 554 samples, and validated via 10-foldcross-validation (FIG. 12).

Conclusion

We have uncovered highly consistent linear relationships between thefrequency of EES and relevant parameters of gait (step height). Therobustness of these relationships allowed us to develop forward modelsand control algorithms that achieved real-time control of locomotion inrats with complete SCI. The linear mapping between these variablesgreatly simplified the requirements for the controller. A singleinput—single output closed loop system was sufficient to achieve theprecise control of foot trajectory during complex locomotor tasks inrats with complete SCI. We thoroughly evaluated the degree ofcontrollability of the system, and revealed unexpected performances thatwere highly consistent across animals, tasks, and time.

Together, these results highlight the potential of real-time controlsystems to optimize EES-induced locomotion. The core principlesunderlying our monitoring and control systems may be condensed to fairlysimple, wearable hardware. The translational application of our methodsfor real-world applications could rely on a gyro attached to the foot ofthe subject, a foot-strike detector (e.g., a force-based switch) and anonboard microcontroller that reads position and calculates proportionalfrequency-corrections online.

Example 5

Phase-Dependent Triggering of Specific Electrodes for Promoting Flexionor Extension During Locomotion

In combination with the aforementioned control of frequency, specificelectrodes may be turned ON and OFF to mimic the phase-dependentactivation of sub-circuits in the spinal cord, namely those related towhole-limb extension (which are mostly active during stance, and whosemotor pools are mostly located along sacral spinal segments) and thoserelated to whole-limb flexion (usually active during swing, and motorpools located in lumbar spinal segments).

Based on this premise, using multi-electrode arrays, we have developed aphysiologically-relevant stimulation paradigm that accurately triggerselectrodes located at lumbar or sacral regions during swing and stancerespectively. Based on real time feedback algorithms that flexiblydetect specific key events of gait, we alternatively activate specificelectrodes at exactly the right sup-phases of gait and induce strongergait patterns, which translate into stronger muscle activation (even formuscles which otherwise get almost no activation), stronger groundreaction forces and more prominent kinematic patterns.

In our design, we uncovered that optimal stimulation timings include:

Triggering sacral electrodes (S1) at footstrike (kinematic event thatdefined the beginning of stance) and maintaining the electrode active atleast until the end of activity the tibialis anterior muscle (once swinghas been initiated).

Likewise, lumbar electrodes (L2) need to be turned ON before theactivation of the tibialis anterior muscle (before the beginning ofswing) and be active at least until mid-swing (for example untilfootstrike).

Such configuration provides the maximum strength to stepping in terms ofground reaction forces, muscle activation and support of body weight,while minimizing coactivation.

Example 6

Triggering of Spinal Electrodes Based on Cortical Recording forPromoting Voluntary Locomotion.

Referring to previous examples 1-5, the aforementioned controllers offrequency and timing of stimulation can be connected to the voluntarymotor intention of the subject animal.

By using a real-time electrophysiology workstation and 32-channelmicro-wire electrode arrays (Tucker-Davis Technologies, Alachua, Fla.,USA) implanted in the rats' sensorimotor cortex hind-limb area, in asingle hemisphere or in both, we could collect information about theanimal's locomotor state, encoded as neuronal multi-unit (MU) activity.

We reliably discriminated the motor intention of the rat into twobehavioral states, ‘rest’ or ‘walk’, using either un-supervised orsemi-supervised machine learning approaches that resulted in intentiondecoding with 50-100 ms time granularity.

The decoding of the motor intention of the rat is immediately (within 50ms) fed to the supervision of spinal stimulating electrodes, switchingON the feedback-feedforward controller and thus achieving the desiredlocomotor pattern. An exemplary representation of the system of thedisclosure is represented in FIG. 15 and explained in its caption.

Moreover, in case the spinal cord lesion does not involve the entiretyof the fibers of the pyramidal tract, we have found that kinematicsensorimotor information modulating with the gait pattern is stillvisible in the recording of the hindlimb sensorimotor cortex, duringboth treadmill and over-ground recordings. This is, for instance, thecase of a severe spinal cord contusion, as shown in FIG. 15.

Cortical recordings contain information related to the gait cycle andcan be used to control or refine in real time the triggering of spinalelectrodes, respectively substituting or co-operating with thekinematic-feedback algorithms aforementioned.

Example 7

Subdural Adaptive Electrical Spinal Cord Stimulation ResolvesLimb-Specific Impairments after Unilateral Spinal Cord Injury

The same experimental procedure described in Example 1 has beenperformed implanting subdural electrodes instead of epidural electrodesand applying a subdural electrical stimulation. All the otherexperimental parameters were the same as in example 1.

Computerized simulation showed increased selectivity of unilateralvoltage fields for subdural implantation (FIG. 16 A).

As shown in FIG. 16B, electrophysiological experiments confirmed thatsubdural stimulation required reduced current threshold, and achievedmore specific unilateral recruitment of motor neurons compared toepidural stimulation

Subdural spinal cord stimulation delivered through the lateralelectrodes of a soft subdural implant (40 Hz, 0.2 ms, 20-50 μA) promotedcoordinated, weight bearing plantar stepping of the paralyzed hindlimbwith improved gait characteristics compared to continuous stimulation(FIGS. 17B-17C).

Subdural adaptive electrical spinal cord stimulation can also be appliedfor bilateral limb paralysis after motor complete spinal cord injury.

The invention claimed is:
 1. A system for determining optimalstimulation parameters for a subject suffering from a neuromotorimpairment and undergoing a process for facilitating locomotor functionscomprising: an epidural and/or subdural electrical stimulation devicefor applying electrical stimulation with adjustable stimulationparameters; one or more sensors; a real-time monitoring system; and asignal processing device, operating a program comprising an automaticcontrol algorithm that interfaces simultaneously with a data flow fromthe real-time monitoring system and the epidural and/or subduralelectrical stimulation device to: acquire feedback signals from saidreal-time monitoring system, where said real-time monitoring systemreceives signals from said sensors, said signals being neural signalsand/or signals providing features of motion of said subject; calculatestimulation parameters via the automatic control algorithm; and provideinstructions to the epidural and/or subdural electrical stimulationdevice for applying an electrical stimulation with calculatedstimulation parameters to said subject, where the automatic controlalgorithm comprises a feedback component and a feedforward component andwherein the automatic control algorithm is developed based on a linearrelationship between a frequency of the electrical stimulation in anepidural and/or subdural space and one or more relevant parameters ofgait.
 2. The system of claim 1, wherein said stimulation parameters areselected from a group consisting of waveform, amplitude, pulse width,and the frequency.
 3. The system of claim 1, wherein the electricalstimulation comprises the frequency between 5 and 120 Hz.
 4. The systemof claim 1, wherein the epidural and/or subdural electrical stimulationdevice is configured to provide phasic stimulation or burst stimulation.5. The system of claim 1, wherein the epidural and/or subduralelectrical stimulation device is configured to apply stimulation to atleast two stimulation sites, where each stimulation site can beindependently turned on or off.
 6. The system of claim 1, wherein theepidural and/or subdural electrical stimulation device further comprisesone or more electrodes, and wherein the electrical stimulation istime-specific (burst-stimulation), and wherein each of the one or moreelectrodes can be individually turned ON and OFF in real time based onexternal trigger signals.
 7. The system of claim 1, wherein theautomatic control algorithm is a real time automatic control algorithm.8. The system of claim 7, wherein the real time automatic controlalgorithm comprises the feedforward component employing one of a singleinput-single output (SISO) model, or a multiple input-single output(MISO) model, where the SISO model comprises changing one electricalstimulation device stimulation parameter to control one gait feature ofthe subject, and where the MISO model comprises adjusting multipleelectrical stimulation device stimulation parameters to obtain a singlegait feature of the subject.
 9. The system of claim 1, wherein theelectrical stimulation comprises lower limb stimulation of the subject.10. The system of claim 9, wherein the lower limb stimulation furthercomprises at least one of lumbar and/or sacral sites.
 11. The system ofclaim 10, wherein the epidural and/or subdural electrical stimulationdevice further comprises electrodes applied on the sacral and lumbarsites which are alternatively activated to promote, respectively,whole-limb extension or flexion of the subject.
 12. The system of claim1, wherein the signal processing device automatically detects gaitevents of the subject, and defines specific sub-phases of gait of thesubject during locomotion.
 13. The system of claim 1, wherein theelectrical stimulation comprises upper limb stimulation.
 14. The systemof claim 13, wherein the electrical stimulation is applied on at leastone cervical site.
 15. The system of claim 14, wherein the epiduraland/or subdural electrical stimulation device further compriseselectrodes applied on the at least one cervical site, where theelectrodes are alternatively activated to facilitate reaching andgrasping.
 16. The system of claim 1, wherein the signals providingfeatures of motion of said subject further comprise one or more ofcoordinates, kinetic information, acceleration, speed, angle, angularspeed, and/or angular acceleration of one or more of hip, knee, ankle,foot, shoulder, elbow, hand, wrist, and/or digits of the subject. 17.The system of claim 1, wherein the neural signals further comprisecortical signals from one or more of a sensory, motor, sensorimotor,and/or pre-motor cortex.
 18. The system of claim 1, wherein theelectrical stimulation is repeatedly applied to the subject during theprocess for facilitating locomotor functions.
 19. The system of claim18, wherein repeatedly applied electrical stimulation is delivered at adetermined pace corresponding to a desired period of the process forfacilitating locomotor functions.
 20. The system of claim 1, wherein theinstructions to the epidural and/or subdural electrical stimulationdevice are provided automatically.
 21. The system of claim 1, whereinthe instructions to the epidural and/or subdural electrical stimulationdevice are provided semi-automatically.
 22. The system of claim 1,wherein the instructions to the epidural and/or subdural electricalstimulation device are provided online.
 23. The system of claim 1,wherein the instructions to the epidural and/or subdural electricalstimulation device are provided offline.
 24. The system of claim 1,wherein the instructions to the epidural and/or subdural electricalstimulation device are provided in real-time.
 25. The system of claim 1,wherein the relevant parameters of gait comprise a step height of thesubject.